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Step-by-Step Structural Equation Modeling (SEM) in SPSS-AMOS

Structural equation modeling (SEM) is a multivariate statistical analysis technique that is used to analyze structural relationships between measured variables and latent constructs. This technique is a combination of factor analysis and multiple regression analysis. This step-by-step tutorial will walk you from data screening to running the causal model with special topics on different types of analysis such as mediation, moderation, and interaction. Kindly check these links for the step-by-step guide of each main step. Hope this is helpful. Data screening - bit.ly/3bEM8FU Exploratory Factor Analysis - bit.ly/33cUPDH Confirmatory Factor Analysis - bit.ly/35gygjP Multivariate assumptions - bit.ly/336dalC Setting up the Causal Model - bit.ly/2ZrspVo Mediation Analysis - bit.ly/3inUNz5 [draft] Moderation Analysis - bit.ly/2ZsoEPs Multi-group Analysis - bit.ly/32jrQ1C

How to Convert Continuous Variables into Categorical in SPSS

Testing the causal model in AMOS may involve having moderator variables that are categorical. This can be done through Multi-group analysis. In cases where available data are continuous (e.g. age, years of experience, etc.), conducting Multi-group analysis is also possible. However, you need to convert first these variables into categorical ones, for them to be used in AMOS. For this example: Age is our continuous variable. We will code high and low values of age. The steps would involve A) Computing for the mean score, and B) Recoding into a different variable. The following sections show the step-by-step process. A. Compute for the Mean Score Go to 'Analyze' -- Descriptive Statistics -- Frequencies. A window will appear. Throw in the continuous variable. Check 'Display frequency tables. Click 'Statistics'. In the 'Central Tendency' section, check for the mean score. Note: It actually depends on how will you categorize this variable. For this example, w

Data Screening for Structural Equation Modeling (SEM)

Data screening is the process of ensuring that your data is clean and is ready for exploratory factor analysis (EFA). The purpose is to ensure that data is usable, reliable, and valid for testing the causal theory. This step-by-step guide will focus on three main issues that need to be addressed when cleaning data for use in the EFA. These are: (A) Missing data in rows, (B) Unengaged responses, (C) Outliers for continuous variables, and (D) Skewness and kurtosis.

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